20 EXAMPLES FROM CANADA, THE UNITED STATES, BRAZIL, THE UNITED KINGDOM, SPAIN, CROATIA, KUWAIT, PALESTINE, CHINA AND AUSTRALIA
Either individually or as a team, in their classes or as part of research projects, these professors, instructors and researchers are systematically testing artificial intelligence (AI) in teaching and learning, recording what worked and what did not work, and sharing their findings with their peers around the world. Contact them directly to get more information on their initiatives, adopt and adapt their tools and approaches to your own circumstances, or share your own experiences with them.
AUTOMATED FEEDBACK AND GRADING
Using pre-programmed responses, automated feedback automatically reacts to learner’s input, giving immediate correction of errors. It is used in higher education in various grading systems that evaluate student responses to assessments and assignments at large scale.
The Open University (UK) - OpenEssayist: An Automated Feedback Tool
To support students in drafting essays, a team from the Open University developed OpenEssayist, an intelligent linguistic analytics tool used in real-time to analyze text in essays and generate automated feedback. OpenEssayist analyzes 3 aspects of an essay; structure, key words/phases and key sentences and then presents a summary to the student that conveys the key points of the essay, allowing students to; 1) reflect on the draft text 2) review how the essay is organised 3) understand how the key terms are being used across the essay and how they combine to form a cohesive discussion. Evaluation of OpenEssayist shows the number of drafts submitted to the tool has a positive impact on the grades awarded for the first assignment. The cohort of students using OpenEssayist achieved significantly higher overall grades than the students in the previous cohort. The complete study is available here.
Contact: Denise Whitelock
Professor of Technology Enhanced Assessment and Learning
The Open University
Johns Hopkins University (USA) – Using Gradescope for Automated Marking of Tests and Exams
A University of California Berkeley professor and three former graduate students developed Gradescope, software to automate components of marking tests and exams, allowing for quick grading in large courses and keeping grading consistent across multiple submissions. To use Gradescope, exams or tests are scanned into the platform and each question is given a rubric. The instructor records the correct answer for each question in the tool allowing it to group together all the correct answers (the same process applies for common incorrect answers). The instructor then assigns feedback to go out to multiple exams at once. Dr. Scott Smith, a professor of Computer Science at Johns Hopkins University used Gradescope for his Principles of Programming Languages course. He found using this automated software significantly reduced the time spent grading and improved the quality of feedback to his students. Although not a feature of Gradescope, Dr. Smith found that he could also use the software to detect cheating by easily identifying identical answers to specific questions. Find the full story here.
Contact: Scott Smith
Professor of Computer Science
Johns Hopkins University
University of Michigan (USA) –Automated Text Analysis Tool
The University of Michigan developed automated software that provides feedback to student’s writen work, eliminating professorial hours on marking and providing feedback on hundreds of assignments. Developing the tool for the university’s M-Write program, with the concept focused on student learning through writing, a software development team used data from three semesters of student responses on writing assignments to create course-specific algorithms. After students submit their essays, the automated text analysis tool is used to evaluate the essay, scanning for features of good writing built into the algorithm. These features are examined using a variety of text analysis techniques, such as vocabulary matching or topic matching. The tool then generates a predicted score which is sent to a writing fellow (students hired to assist with the revision process) to verify. After this verification, the score is made available to the student, who has the opportunity to revise and resubmit the piece based on the combination of feedback from the automated tool, assigned writing fellow and peer review. Read more about automated text analysis tool here.
Contact: Anne Ruggles Gere
Gertrude Buck Collegiate Professor of Education
Professor of English Language and Literature
University of Michigan
INTELLIGENT TUTORING SYSTEMS (ITS)
Intelligent tutoring systems work to imitate a human tutor’s behaviour and guidance. These systems learn while being used, interpreting complex student answers to determine when a student does not grasp a concept and offers suggestions to help the student understand the content.
The University of California, San Diego (USA) - MOOCs and Intelligent Tutoring Systems
The University of California, San Diego, computer science Professor Pavel Pevzner and colleagues designed the MOOC course Introduction to Genomic Data Science as an adaptive Intelligent Tutoring System (ITS) for the edX platform. To lead students through a personal learning pathway, allowing evaluation at every stage of the student’s learning, the course has incorporated quizzes and “just in time” exercises to assess if the student has understood the content. If a student answers incorrectly, they are directed to a remedial site to assist with content understanding and attainment. The course also provides coding challenges to assess each student’s progress and to replace the basic multiple-choice quizzes prevalent in most MOOCs. Integrating an ITS into a MOOC format addresses the big issue of course modification and updating, as the ITS platform is designed to support easy, continual updating, especially useful in topics where students experience difficulty. According to Pevzner: “The goal is to represent each learner as a pathway through the tutoring system and to analyze those digital paths across thousands of students – producing an avalanche of data that can be used to continually adjust the coursework to meet the needs of all learners”. Full story here.
Contact: Pavel Pevzner
Professor of Computer Science, Department of Computer Science & Engineering
University of California San Diego
Al-Azhar University (Palestine) - Using Intelligent Tutoring Systems for Learning Computer Theory
To support the individual learning needs and styles of students in the computer science and mathematics programs at Al-Azhar University in Gaza, Palestine, Mohammed Nakhal and Professor Samy S. Abu Naser incorporated an intelligent tutoring system (ITS) into a computer theory class. Using artificial intelligence, the ITS stores data on student behaviour to use it for analysis to adapt to each student’s learning needs. Using the premise of a virtual instructor, the ITS provides the content in a specific sequence according to student data on learning style, student behaviour, motivation level and preferences. Based on the stored student data, customized feedback on questions answered correctly and incorrectly is provided. If, for example, an answer is incorrect, the student is directed to the material to help them solve the question at hand. When the student answers correctly, the system congratulates them and brings them to the next level of the lesson. Read the case study here.
Contact: Samy Abu Naser
Professor of Artificial Intelligence, Department of Information Technology
Learning analytics are used to monitor student performance by employing big data from teaching and learning activities. AI can track student performance, predict drop out risk and allow for interventions and student support when required.
Arab Open University (Kuwait) – Using Predictive Analytics to Reduce Dropout Rates and Increase Completion Rates
One of Arab Open University’s (AOU) main objectives is to double the number of students over the next 5 years. To accomplish this, they are using IBM Watson Analytics to improve student retention and identify students at risk of dropping out. IBM Watson Analytics uses artificial intelligence and machine learning algorithms to sense, predict, infer and provide recommendations. With insights revealed by the analytics, AOU can identify vulnerable students by pinpointing key drivers of student attrition. One of the most promising outcomes is the decision support dashboards, such as the one related to Student Risk Factor (SRF), a score which is composed of the student’s current GPA, progression rate, and the number of warnings received. By identifying at risk students, the dashboard acts as an early alert system, enabling the AOU management to take corrective actions and targeted initiatives to help struggling students get back on track for success — increasing retention and boosting student numbers.
Contact: Ashraf S. Hussein
Vice President for Education and Information Technology
Dean of the Faculty of Computing and Information Technology
Arab Open University
Liverpool John Moores University (UK) –Data Analysis Techniques in Predicting Student Performance in Massive Open Online Courses (MOOCs)
MOOCs disseminate free online educational content to thousands of learners from various education backgrounds through platforms such as Coursera, FutureLearn, edX, and Udacity from a range of universities, attracting a broad spectrum of students to their courses. Despite the abundance and popularity of MOOCs, completion rates continue to be low. Professor Glyn Hughes and Chelsea Dobbins at Liverpool John Moores University utilize data analysis on student performance to predict trends of students at risk of attrition. eRegister, an attendance monitoring tool, captures and analyzes data which are transformed into a dashboard of statistics used by MOOC developers. High and active engagement, interaction, and attendance are indicators of student retention in a course and are important in identifying students in danger of dropping out, thus providing potentially useful data to increase MOOC completion rates. Read the full study here.
Contact: Chelsea Dobbins
Senior Lecturer, School of Computing and Mathematical Sciences
Liverpool John Moores University
AI can support individual students throughout their studies, from personal learning assistants to providing student advice, as shown in the four examples below.
University of Southern California (USA) - Personal Assistant for Life Long Learning (PAL3)
To assist in retention of information and to build and maintain student skills, a team from the Institute for Creative Technologies at the University of Southern California, designed the PAL3 tool to provide intelligent support for on-the-job training and lifelong learning. This intelligent learning guide is designed to understand students’ current knowledge and skill set, analyze gaps in their learning and recommend learning resources to help accomplish their goals. The main features of the PAL3 tool include a continuous learning record, the PAL mentoring agent, a library of learning resources with algorithms for recommending these resources that link to both intelligent tutors and traditional learning resources, and mechanisms to promote engagement. The goals for PAL3 are; 1) to prevent skill decline and increase information retention 2) to practice and build knowledge and skills 3) to track skills persistently and 4) to monitor, engage and motivate the student. Read more on PAL3 here.
Contact: Bill Swartout
Chief Technology Officer, Institute for Creative Technologies
University of Southern California
Deakin University (Australia) - IBM’s Supercomputer Watson Provides Student Advice
Cognitive-computing technology, available 24/7, allows students at Deakin University to ask IBM Watson questions about administrative and course information in natural language in place of searching through keyword-based FAQs. Watson is asked over 1600 questions a week about an assortment of topics, such as admissions, enrollment, tuition and fees, financial assistance, student housing, extracurricular skills development, health and wellness, facilities, job placement, employment preparation, job skills assessment and academic help. Deakin University predicts the use of this tool will boost enrollment by up to 10% as a result of a 20% increase in student satisfaction. Read more about this tool here.
Contact: William Confalonieri
Chief Digital Officer
Georgia State University (USA)- Encouraging Student Registration with Personalised Text Messaging
Georgia State University teamed up with AdmitHub, an edtech start up, to address their “summer melt” issue. “Summer melt” refers to the phenomenon of high school graduates who plan to attend college, but fail to enroll in the fall due to a variety of factors such as costs and lack of support. As texting is a student’s preferred choice of communication, they developed an artificial intelligent chatbot named Pounce (named after the university mascot) to provide instant guidance to college-related questions, such as when is my tuition due? and where do I send my SAT scores? After a randomized controlled trial where half of the incoming freshman class had access to Pounce and the other half received communications via e-mail and post mail, Georgia State found the group of students who texted with Pounce (over 200,000 exchanges in total) had a 21.4% lower summer melt rate compared to the control group. Read the full case study here.
Contact: Timothy Renick
Vice Provost and Vice President for Enrollment Management and Student Success
Georgia State University
The Open University of Hong Kong (China) - The i-Counseling System
The Open University of Hong Kong (OUHK) enrolls 20,000 students a year in both part- and full-time studies using face-to-face, distance education and online learning to reach its various student populations. To help with the volume of student inquiries on topics such as programs and courses, learning modes, and study plans and paths, as well as provide 24-hour access to information, OUHK developed an intelligent counseling system called the i-Counseling System. This system uses i-Ambassador, an animated character with multilingual and text-to-speech capabilities to offer students a better and more natural inquiry experience. There are two modules to this system, academic counseling and academic advisement. The academic counseling module handles general queries from prospective students, on topics regarding program/course information, career questions and learning modes. The academic advisement module deals with more explicit inquiries from current students, on program particulars, study plans, and graduation information. Read the full case study here.
Contact: Eva Tsang
Director of Educational Technology and Publishing
Open University of Hong Kong
ADAPTIVE GROUP FORMATION
Selecting and grouping students together based on factors such as similar interests or cognitive level, can offer effective mixes of knowledge, ideas and skills.
Universitat Politècnica de València (Spain) –Heterogeneous Team Formation
Higher education institutions are recognizing the pivotal role they play in developing essential teamwork skills among students to help prepare them for success in the workplace, using skills such as positive team dynamics, clear communication and interpersonal collaboration. The Universitat Politècnica de València in Spain addressed this need by developing a tool to create diverse, classroom-based teams grounded in theory that identifies eight different behavioural patterns and roles of successful teams. The tool calculates and proposes optimal team configurations and gathers feedback from team members on the roles of team members to be used in future team formation tasks. The team formation tool is more successful than traditional team methods in facilitating different teamwork aspects, such as student satisfaction, team dynamics, and cooperation and coordination. Access the study here.
Contact: Juan M. Alberola
Universitat Politècnica de València
VIRTUAL REALITY AND SIMULATIONS
Virtual and mixed reality, simulations and games can provide real-life digital representations of people, environments and objects, encouraging student engagement and interest while providing an immersive, responsive and adaptive learning setting.
Queen’s University (Canada) – Simulating Real-World Communication Situations
Ensuring students acquire comprehensive expertise with advanced theoretical knowledge and soft professional skills is a top priority for professional schools in law, engineering, medicine and business. This is why Queen’s University is using Ametros Learning’s intelligent simulations powered by IBM Watson’s cognitive-computing tool to focus on case-based teaching through simulations of real-world challenges, allowing students to develop and hone decision-making and problem-solving abilities. Students use textual, visual and oral communication on the platform to practice “real” communication interactions with artificially intelligent characters. These characters can take on the role of a client, vendor, patient, peer, and/or team member. Characters involve students in contextual interactions and provide individualized feedback on student work. The simulation platform creates a rich, risk-free simulated environment in their chosen field, where students learn through experience. More on this here.
Contact: Bill Flanagan
Dean and Professor of Law
UNISINOS (Brazil) -Combining Virtual Reality and Artificial Intelligence
UNISINOS, a private university in Brazil, decided to create an interactive, animated, intelligent virtual world (called Watt world) to teach about the social, economic, scientific and technological progress during the initial phase of the Industrial Revolution. The Watt virtual world provides an alternative to the traditional way of teaching history by employing intelligent pedagogical agents representing James Watt (a Scottish inventor who was fundamental in initiating change during the Industrial Revolution) as well as other characters found in the mines and factories of the time. These characters are capable of interacting with students to solve problems, propose missions and talk about different artifacts and their purpose and importance for the historical period. Using basic gamification techniques, students undertake missions of recognition and collection of information in the scenario to use for later analysis and reflection. Read more about this virtual world here (pages 3-12). (Paid access).
Contact: João Carlos Gluz
Professor, Post-Graduation Program in Applied Computing (PPGCA)
AI agents or bots as they are commonly known, can be used to moderate and/or facilitate group discussions by being either a virtual peer or expert participant.
Georgia Institute of Technology (USA) – Creating an AI Teaching Assistant
A Georgia Tech computer science professor created a virtual teaching assistant (TA) named Jill Watson, based on the IBM Watson platform, to help answer the more than 10,000 forum posts in his online course of over 300 students. The virtual TA takes routine essential questions , such as queries about proper file formats, data usage, and the schedule of office hours - questions with firm, objective answers, while the human TAs handle the more complex questions. For the first few weeks, Jill Watson gave irrelevant answers which were posted to a site only the developers could see. They worked to overcome Jill’s issues and soon her answers had 97% certainty and they were sent directly to students with no intervention or vetting by developers. Students were unaware their TA was actually a computer.
Contact: Ashok K. Goel
Professor of Computer Science and Cognitive Science
School of Interactive Computing
Georgia Institute of Technology
Athabasca University (Canada) – Freudbot to Engage Student Learning
To assist in motivating and engaging distance students, Associate Professor of Psychology, Bob Heller, developed a “Freudbot”, an artificial intelligent conversational agent centred on the historical figure Sigmund Freud. AI conversational agents, or chatbots, converse using the widely accepted rules of conversation, including turn-taking. At the onset, students interacted with the Freudbot by typing comments or questions about Freud’s life and theory in a text window and the Freudbot responded in the first person. The Freudbot was then used in the virtual world of Second Life, to determine if an avatar improved the interaction and conversational experience. Conversations in virtual worlds with the Freudbot reflected a higher degree of social presence in comparison to conversations outside of virtual spaces. Learn more here.
Contact: Bob Heller
Associate Professor, Psychology
PERSONALIZED AND ADAPTIVE LEARNING ENVIRONMENTS
Personalized learning is a phrase that encompasses educational content tailor-made to the unique needs of individual students, including adaptive learning and other methods to individualize a student’s path to a credential. Adaptive learning actively customizes content to each individual’s needs, strengths and weaknesses and tracks student’s knowledge and performance in real time to recommend next steps in a pathway to learning objective mastery.
Kent State University (USA) – ALEKS: Adaptive Learning System for Math Success
Plagued with low success rates in its beginner math courses, Kent State University turned to a student-centred adaptive technology called ALEKS (Assessment and Learning in Knowledge Spaces) as an integral part of redeveloping its math program. ALEKS is an artificially intelligent assessment and learning system used to adapt to the students’ prior knowledge and learning history as they work through the course. As they progress, the software adjusts the difficulty of the math problems based on correct and incorrect answers. When comparing students from the traditional lecture-based programs to the students in the ALEKS program, ALEKS students showed significant improvement, with the doubling of A grades.
Contact: Andrew Tonge
Chairman, Department of Mathematics
Kent State University
University of Zagreb (Croatia) – Spaced Repetition Software for Learning Japanese
Spaced repetition is a learning technique that uses increasing intervals of time between learning new content and the review of previously learned content. It is usually applied in language learning to accommodate the large amount of content to be retained in the student’s long-term memory. Software can adapt to the student’s prior knowledge and use that information to help the student memorize characters, vocabulary, and phrases by determining how frequently the student must review content so it stays in their long-term memory. Faculty at the University of Zagreb harnessed this technique to teach Japanese to a group of 27 undergraduate students through the language learning application, Memrise. Students who regularly used Memrise for learning Japanese had 40% better grades than those who didn’t at the end of two consecutive semesters. Read the research paper here.
Contact: Sara Librenjak
Lecturer of Japanese and Korean Language, Department for Linguistics
University of Zagreb
University of New South Wales (Australia) –Adaptive Tutorials in a MOOC
Professor Ganga Prusty of the University of New South Wales began teaching a course in first year engineering mechanics with a 31% failure rate. In collaboration with Smart Sparrow, a company that specializes in adaptive and personalized learning technology, they designed and integrated adaptive tutorials into the course, featuring interactive simulations, applied adaptive feedback and pathways adapted to the students’ level of understanding. After reducing the course failure rate by 77% and increasing the number of A grades by 350%, Dr. Prusty and colleagues set out to use adaptive leaning technologies in a MOOC called Through Engineers’ Eyes: Engineering Mechanics through Experiment, Analysis and Design to teach fundamental engineering principles. The adaptive tutorials offer a personalized learning experience for each student in an environment that is typically one-size-fits-all learning for thousands of people. In April 2016, the MOOC was offered for the first time with over 7,000 registrants from more than 100 countries. The end-of-course survey revealed 85% of participants felt the adaptative tutorials were useful for applying concepts and helped them to evaluate their learning effectively. Read more on this story here.
Contact: Gangadhara Prusty
Professor, Mechanical & Manufacturing Engineering
University of New South Wales
As the growth and popularity of online courses and degrees grows, so does the need to accommodate tests and exams online, resulting in the proliferation of the number of for-profit and in-house developed online proctoring tools.
Stevens Institute of Technology (USA) - Facial Recognition Techniques for Virtual Proctoring
A team from Stevens Institute of Technology’s School of Engineering and Science piloted a virtual laboratory tool with biometric authentication used to identify the student and monitor their actions via remote proctoring using facial recognition techniques to an undergraduate mechanical engineering course. When using the virtual laboratory tool, the students log in by scanning their faces with a web camera. While performing a laboratory assessment, the student sits in front of the camera and the virtual laboratory tool monitors their facial expressions and head motions in order to identify suspicious behaviours. Upon detection of such behaviours, the tool records a video for further analysis by the laboratory administrator. The virtual lab tool with a virtual proctor works well and provides a high degree of accuracy in detecting suspicious behaviour during assessments. Read the case study here.
Contact: Sven K. Esche
Associate Director and Director of Graduate Programs
Department of Mechanical Engineering
Stevens Institute of Technology
As illustrated above, there are a number of areas where artificial intelligence can be employed in higher education to enhance student learning, empower educators and optimize operations. These applications and examples illustrate institutions taking advantage of these opportunities, promoting enhanced learning and innovation consistent with the purpose and integrity of higher education.